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EugeneTes 3314112bb9 ingest: Stanford SWEPR widening-gap study and AI-in-SDLC adoption pitfalls

Add two new sources with summaries, new concepts (developer-as-agent-manager,
review-is-the-new-bottleneck), new entities (SWEPR, Nikolai Sheiko), and a
query on the Stanford source; update related concept pages, overview, index,
and log.

2026-07-31 08:33:56 +02:00

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SWEPR (Stanford Software Engineering Productivity Research)

#entity

Summary

Research group at Stanford University measuring software-engineering productivity from private Git data — 600+ companies, ~100k–120k engineers since 2022 — using an ML model that replicates a panel of expert code reviewers. Public face: researcher Yegor Denisov-Blanch. Source of the corpus's only quantitative outside study of AI's productivity impact.

Current Understanding

Site: https://softwareengineeringproductivity.stanford.edu/ — offers an "AI Practices Benchmark" and "AI Impact" research to participating companies.
Known for three results: the widening-gap DiD analysis (46 vs 46 teams, gap 4.8% → 19%, 4×, Apr 2023–Jul 2025), the ~15–20% net average gain figure (after rework; 30–40% gross), and the earlier "ghost engineers" finding (~9.5% of engineers show virtually no verifiable output).
Methodology is peer-reviewed (arXiv 2409.15152, 2502.20747); the headline AI-impact analyses are talk/deck-published (Sept 2025 AI Conference deck "Will AI Replace Software Engineers?").
Their measurement philosophy — functionality delivered, not commits or LOC — aligns with the corpus's own suspicion of volume metrics (make-more-cheap-code: generation volume is the cheap part).

Evidence

All claims and links — 2026-07-30-stanford-swepr-widening-gap.

Sources: 2026-07-30-stanford-swepr-widening-gap
Concepts: levels-of-ai-usage (team-level twin of the mastery gap), context-as-scarce-resource (codebase-size finding), make-more-cheap-code (review-time shift)
Queries: 2026-07-30-stanford-widening-gap-source
Entities: allie-miller (her prediction, their measurement)

Contradictions / Uncertainty

Data is proprietary and opt-in — no outside replication possible; participating companies may skew toward the measurement-friendly. Status: tentative.
The widening-gap analysis itself is not peer-reviewed as of 2026-07-30. Status: tentative.

Next Questions

Track whether the DiD analysis lands in a peer-reviewed venue.
What do they say causes the gap (tooling vs hygiene vs practices)? The talks assert "quality of usage" without decomposing it.

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